Machine Learning:
New Perspectives for Science

The rise of “intelligent” technology is transforming engineering, industry and the economy at an increasing pace and on an unprecedented scale. At the core of this revolution are breakthroughs in the field of machine learning which allow machines to perform tasks that, until recently, could only be performed by humans. Less prominently discussed, developments in machine learning have the potential to transform science at an equally fundamental level. While machine learning methods have been used in the past to tackle isolated prediction problems, recent breakthroughs open up an exciting new opportunity: Automated inference methods will become increasingly useful in the process of scientific discovery itself, supporting scientists in identifying which hypotheses to test, which experiments to perform, and how to extract principles describing a broad range of phenomena.

The aim of this cluster is to enable machine learning to take a central role in all aspects of scientific discovery and to understand how such a transformation will impact the scientific approach as a whole.

What is a Cluster of Excellence?

The Excellence Strategy of the federal and state governments aims to promote outstanding science and improve the quality of German universities and research institutions. The Excellence Strategy comprises two funding lines: The Clusters of Excellence and the Universities of Excellence. Clusters of Excellence are collaborative research projects in internationally competitive research fields at selected locations (universities or university alliances). The development and implementation of the procedure in this funding line is the responsibility of the German Research Foundation (DFG).

In the first funding period (January 2019 to December 2025) a total of 57 Clusters are being funded with an annual budget of 385 Mio. Euro. In the currently launched second funding period (January 2026 to December 2032) up to 70 Clusters of Excellence can be funded with an annual budget of 539 Mio. Euro.